Conventionally, there has been known a position sensing system that uses an RFID positioning device and a security camera. For example, Japanese Patent Laid-Open No. 2006-311111 discloses such a system as described above. In this conventional system, an authenticator has an active tag. The authenticator takes an image of an area being monitored using a security camera and detects the active tag using an RFID positioning device. Then, the position of a suspicious individual within the area is identified by comparing camera positioning data acquired using the security camera with RFID positioning data acquired using the RFID positioning device.
However, in the conventional system, there may be a case that, for example, a monitoring target enters a dead angle of the security camera and, therefore, the monitoring target cannot be imaged with the security camera and camera positioning data cannot be acquired. Thus, the system has not been able to specify the position of the monitoring target within an image and has had difficulty in keeping track of the movement of the monitoring target.
In addition, there has conventionally been a technique disclosed as this type of technique in, for example, Japanese Patent Laid-Open No. 2005-141687.
Japanese Patent Laid-Open No. 2005-141687 describes using the probability density distribution PtV of the position of a target obtained from image information at time “t”, the probability density distribution PtA of the position of the target obtained from sound information at time “t”, and a weighting factor k1(t), thereby evaluating a likelihood distribution “F”, in which the probability density distribution PtV and the probability density distribution PtA are integrated, by Expression 1 shown below.
[Expression 1]F=k1(t)PtV+(1−k1(t))PtA  (Expression 1)
In Japanese Patent Laid-Open No. 2005-141687, the weight of input information (image information or voice information) for the probability density distribution is increased with an increase in the maximum probability density value of the probability density distribution by setting a weighting factor k1(t) to a value expressed by Expression 2 shown below. Conversely, the weight of input information (image information or voice information) is decreased with a decrease in the maximum probability density value of the probability density distribution.
                    [                  Expression          ⁢                                          ⁢          2                ]                                                                                  k            1                    ⁡                      (            t            )                          =                              max            ⁡                          (                                                P                  t                  V                                ⁡                                  (                  ϕ                  )                                            )                                                          max              ⁡                              (                                                      P                    t                    V                                    ⁡                                      (                    ϕ                    )                                                  )                                      +                          max              ⁡                              (                                                      P                    t                    A                                    ⁡                                      (                    ϕ                    )                                                  )                                                                        (                  Expression          ⁢                                          ⁢          2                )            
That is, the maximum probability density value of the probability density distribution is used as a guideline for reliability, in order to obtain a probability density distribution in which the probability density distribution of image information and the probability density distribution of voice information are integrated. Then, by estimating an object position from this integrated probability density distribution, it is possible to improve the accuracy of object position estimation.
Now an image of integration processing applied in Japanese Patent Laid-Open No. 2005-141687 will be described using FIG. 30.
FIG. 30(A) illustrates an example of a probability density distribution in a case where position coordinates of one point are obtained as observed position data (i.e., measurement results with respect to one certain event). FIG. 30(B) illustrates an example of a probability density distribution in a case where position coordinates of two points, identical to each other in the possibility of a target being present, are obtained as observed position data. Note that under normal circumstances, an object position on a two-dimensional plane is determined in object position estimation and, therefore, observed position data on a two-dimensional plane is obtained. Consequently, a probability density distribution determined from the observed position data is also distributed on a two-dimensional plane. For the sake of simplicity, however, FIG. 30 illustrates examples of positioning on a one-dimensional line segment. Also in examples to be described hereinafter, positioning on a two-dimensional plane may be explained using examples of positioning on a one-dimensional line segment for the sake of simplicity.
Here, assume, for example, that such observed position data as illustrated in FIG. 30(A) is obtained according to both image information and voice information and that a probability density distribution illustrated in FIG. 30(A) is obtained as a probability density distribution based on the observed position data. Then, the probability density distribution based on image information and the probability density distribution based on voice information are integrated using almost the same weighting factor.
On the other hand, assume, for example, that a probability density distribution based on image information is the one illustrated in FIG. 30(A). In addition, such observed position data as illustrated in FIG. 30(B) is obtained from voice information and a probability density distribution illustrated in FIG. 30(B) is obtained as a probability density distribution based on the observed position data. Then, the two probability density distributions are integrated using a weighting factor by which the probability density distribution obtained from voice information is multiplied and which is smaller than a weighting factor by which the probability density distribution obtained from image information is multiplied.
Incidentally, the technique disclosed in Japanese Patent Laid-Open No. 2005-141687 is such that a plurality of probability density distributions acquired with different sensors is integrated using weights according to reliability. Accordingly, the technique is considered to be certainly effective in improving the accuracy of object position estimation.
However, the weighting factors shown in Japanese Patent Laid-Open No. 2005-141687 are determined simply according to the maximum density in a probability density distribution. Consequently, if each probability density distribution itself serving as a source of integration is mistaken, then weighting itself is also mistaken. As a result, position estimating accuracy is also degraded.